系统梳理高精地图车道拓扑推理技术演进与现状
A Concise Survey on Lane Topology Reasoning for HD Mapping
- 按方法分为规则建模、航拍图像和车载传感器三类
- 基于Transformer和图神经网络的模型性能显著提升
- 适合自动驾驶与高精地图领域研究者参考
车道拓扑推理技术在高精度（HD）地图与自动驾驶中至关重要。尽管近年来进展显著，但缺乏对相关工作的系统性综述。本文系统回顾了该领域的演进与现状，将其方法分为三类：基于过程建模、基于航拍图像和基于车载传感器的方法。分析了从早期规则方法到现代基于Transformer、图神经网络（GNN）等深度学习架构的学习方法的发展。评估标准包括道路级指标（APLS、TLTS得分）和车道级指标（DET、TOP得分），并在OpenLane-V2等基准数据集上进行性能对比。识别出数据集稀缺与模型效率等关键挑战，并展望未来研究方向。本综述为研究人员与从业者提供了理论框架、实践实现与新兴趋势的深入洞察。
原文摘要 · Abstract (English)
Lane topology reasoning techniques play a crucial role in high-definition (HD) mapping and autonomous driving applications. While recent years have witnessed significant advances in this field, there has been limited effort to consolidate these works into a comprehensive overview. This survey systematically reviews the evolution and current state of lane topology reasoning methods, categorizing them into three major paradigms: procedural modeling-based methods, aerial imagery-based methods, and onboard sensors-based methods. We analyze the progression from early rule-based approaches to modern learning-based solutions utilizing transformers, graph neural networks (GNNs), and other deep learning architectures. The paper examines standardized evaluation metrics, including road-level measures (APLS and TLTS score), and lane-level metrics (DET and TOP score), along with performance comparisons on benchmark datasets such as OpenLane-V2. We identify key technical challenges, including dataset availability and model efficiency, and outline promising directions for future research. This comprehensive review provides researchers and practitioners with insights into the theoretical frameworks, practical implementations, and emerging trends in lane topology reasoning for HD mapping applications.
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